Application of Data Processing in Company Customer Management based on K-means Clustering Algorithm

Abstract

At present, many researchers and companies divide customer groups according to Kmeans, and formulate targeted marketing strategies according to different customer groups. The paper discusses two kinds of K-means and how different companies should choose in the real life. For the data choosing parts, both -k mean method involves RFM models as a parameter. Both of the two methods used normalizing to standard all dependent variables less than one. The difference between the two methods is that the second improved one including Malicious and the difference between first and last purchase. Also, the improved k-means after normalizing the dependent variable, that add all them up as a CLV parameter. Then the improved k-mean mainly finds the relationship between CLV value and optimal center. Finally, this article recommends that companies with a larger customer base or who need to clarify customer needs need to use the second improved k-means in the article. For smaller companies, it is sufficient to use the first category k-means.